Sediment transport and shear stress partitioning in a vegetated flow
Bibliographic record
Abstract
Abstract Vegetation is a common feature in natural coastal and riverine water ways, interacting with both the water flow and sediment transport. However, the physical processes governing these interactions are still poorly understood, which makes it difficult to predict sediment transport and morphodynamics in a vegetated environment. We performed a simple experiment to study how sediment transport responds to the presence of flexible, single‐blade vegetation, and how this response is influenced by the vegetation density. We found that the skin friction and sediment transport are reduced in a plant patch, and that this effect is larger for denser vegetation. We then evaluated several methods to calculate the skin friction in a vegetated flow, which is the key to sediment transport prediction. Among these, the inversion of bed load transport formulas and the Einstein and Banks (1950) methods appeared to produce the most reasonable values of the skin friction. Finally, we suggest using the parameter α , which is the ratio of the skin friction computed by these methods to the total bed shear stress, to make more realistic sediment transport predictions in morphodynamic models.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".